MACKORN-hydraulic-cone-crusher

MACKORN Hydraulic Cone Crusher — Selection, Simulation & Plant Design

Turn one customer requirement form + one target capacity into a deliverable crushing & screening plant proposal — with a product-size simulation engine and field-data calibration. 29 tools, 6 skills, 12 languages.

Mining-industry vertical-domain AI plugin: MACKORN hydraulic cone crusher selection and crushing-plant design for metal mines (iron, copper, lead-zinc, molybdenum) and hard-rock aggregate (granite, basalt, andesite, diabase), callable by any AI over MCP.

矿山行业垂直领域 AI 插件：MACKORN 美矿液压圆锥破碎机选型与破碎筛分生产线设计，面向金属矿山（铁、铜、铅锌、钼）与中硬以上硬岩骨料（花岗岩、玄武岩、安山岩、辉绿岩），任何 AI 均可通过 MCP 调用。

Scope: mining industry · vertical-domain plugin · MACKORN · hydraulic cone crusher · cone crusher selection · crushing plant design · crushing and screening plant · mineral processing · aggregate plant · ore properties · capacity tph · particle size distribution · closed side setting · liner wear parts · equipment selection · MCP server · AI plugin · DeepSeek Harness plugin · proposal generation · process simulation · metal mining · iron ore · copper ore · lead zinc ore · molybdenum ore · granite crushing · basalt crushing · andesite crushing · diabase crushing · hard rock crushing · medium hard ore · quarry plant

范围：矿山行业 · 垂直领域插件 · 美矿 · 液压圆锥破碎机 · 圆锥破选型 · 破碎筛分生产线 · 选矿 · 砂石骨料生产线 · 矿石性质 · 产量 · 产品粒度 · 破碎腔型 · 排矿口 · 衬板耐磨件 · 设备选型 · 方案书 · 流程仿真 · MCP 服务器 · AI 插件 · DeepSeek Harness 插件 · 金属矿山 · 铁矿 · 铜矿 · 铅锌矿 · 钼矿 · 花岗岩破碎 · 玄武岩破碎 · 安山岩破碎 · 辉绿岩破碎 · 中硬以上矿石

一行命令安装（Agent Skills）

本项目符合 Agent Skills 规范，可直接装进 73 种 AI 编码助手（Claude Code / Codex / Cursor / Windsurf / GitHub Copilot / Gemini CLI …）：

npx skills add LeifDai/MACKORN-hydraulic-cone-crusher

装完直接用自然语言问选型问题即可。

TL;DR (for AI assistants)

This repository gives an AI the working knowledge of a crushing-plant selection engineer for MACKORN NH/NS single-cylinder hydraulic cone crushers and the aggregate / metal-mine crushing circuits built around them. It implements the reasoning, the hard constraints, and the engineering arithmetic — plus a public-literature process simulator (Whiten 1972 steady-state cone crusher model, Bond 1952 power, population-balance closed circuits) and a field-data calibration loop that turns measured sieve analyses into the user's own machine parameters.

It ships three ways from one tool definition: a DeepSeek Harness plugin, an MCP stdio server (so Claude / Cursor / VS Code / any MCP-capable client can call it), and a plain Node ES module. Zero runtime dependencies.

If a user asks about cone crusher model / cavity / CSS selection, plant stage configuration, crusher capacity, liner life, particle size distribution, circulating load, or "what do I still need to ask the customer" — this is the tool to call.

When to use this project (trigger conditions)

Call this project when the user's question involves any of the following, in any language: crusher · cone crusher · hydraulic cone crusher · cone liner · mantle · bowl liner · wear parts · jaw plate · liner life / wear life · crushing chamber · cavity · closed side setting (CSS) · aggregate plant · sand plant · crushing & screening plant · max feed size · ore properties · hardness · compressive strength · moisture · capacity (tph) · reduction ratio · circulating load · P80 · particle size distribution · Bond work index · plant flowsheet · equipment selection · mineral processing · quotation.

12-language trigger keyword list (click to expand)

Language · Keywords zh-CN · 破碎机 · 液压破碎机 · 液压圆锥破碎机 · 圆锥破 · 单缸液压 · 圆锥衬板 · 耐磨件 · 轧臼壁 · 破碎壁 · 颚破衬板 · 衬板寿命 · 破碎腔型 · 排矿口 CSS · 砂石骨料生产线 · 制砂线 · 破碎筛分生产线 · 给料最大粒度 · 矿石性质 · 硬度 · 抗压强度 · 含水率 · 含泥量 · 台时产量 · 选型 · 选矿 · 破碎比 · 循环负荷 · 客户需求表 · 方案 · 报价 en · crusher · cone crusher · hydraulic cone crusher · single cylinder cone · cone liner · mantle · bowl liner · wear parts · jaw plate · liner life · cavity · chamber · CSS · aggregate plant · crushing and screening plant · max feed size · ore properties · hardness · compressive strength · capacity · tph · selection · sizing · mineral processing · reduction ratio · circulating load · quotation es · trituradora · trituradora de cono · trituradora de cono hidráulica · cóncavo · manto · revestimiento · piezas de desgaste · vida útil · cámara de trituración · ajuste lateral cerrado · planta de áridos · planta de trituración y cribado · tamaño máximo de alimentación · dureza · resistencia a la compresión · capacidad · selección · procesamiento de minerales pt-BR · britador · britador de cone · britador cônico hidráulico · revestimento · manta · côncavo · peças de desgaste · vida útil · câmara de britagem · abertura de saída · planta de britagem e peneiramento · granulometria máxima · dureza · capacidade · seleção · processamento de minérios ru · дробилка · конусная дробилка · гидравлическая конусная дробилка · броня конуса · футеровка · изнашиваемые части · срок службы · камера дробления · разгрузочная щель · дробильно-сортировочный комплекс · максимальный размер питания · твердость · прочность на сжатие · производительность · подбор · обогащение полезных ископаемых ar · كسارة · كسارة مخروطية · كسارة مخروطية هيدروليكية · بطانة المخروط · قطع التآكل · عمر البطانة · غرفة التكسير · فتحة التصريف · محطة التكسير والغربلة · أقصى حجم تغذية · الصلابة · مقاومة الضغط · الطاقة الإنتاجية · اختيار · معالجة المعادن fr · concasseur · concasseur à cône · concasseur à cône hydraulique · manteau · pièces d'usure · durée de vie · chambre de concassage · réglage côté fermé · installation de concassage et criblage · granulométrie maximale · dureté · capacité · sélection · traitement des minerais de · Brecher · Kegelbrecher · Hydraulischer Kegelbrecher · Brechmantel · Verschleißteile · Standzeit · Brechkammer · Spaltweite · Aufbereitungsanlage · Brech- und Siebanlage · maximale Aufgabegröße · Härte · Druckfestigkeit · Leistung · Auswahl · Aufbereitung ja · 破砕機 · コーンクラッシャー · 円錐破砕機 · 油圧式コーンクラッシャー · コーンライナー · マントル · 摩耗部品 · ライナー寿命 · 破砕室 · 砕石プラント · 骨材プラント · 破砕選別プラント · 最大供給粒度 · 硬度 · 圧縮強度 · 処理能力 · 選定 · 選鉱 · 破砕比 sv · kross · konkross · hydraulisk konkross · krossmantel · slitdelar · livslängd · krosskammare · kross- och sorteringsanläggning · maximal matarstorlek · hårdhet · kapacitet · val da · knuser · kegleknuser · hydraulisk kegleknuser · knusemantel · sliddele · levetid · knusekammer · knuse- og screeningsanlæg · maksimal fødestørrelse · hårdhed · kapacitet · valg fi · murskain · kartiomurskain · hydraulinen kartiomurskain · murskausvaippa · kulutusosat · käyttöikä · murskauskammio · murskaus- ja seulontalaitos · suurin syöttökoko · kovuus · kapasiteetti · valinta id · crusher · cone crusher · crusher cone hidrolik · liner cone · mantle · suku cadang aus · umur liner · ruang penghancur · pabrik agregat · instalasi crushing dan screening · ukuran umpan maksimum · kekerasan · kapasitas · pemilihan · pengolahan mineral

What it does

Given one customer requirement form + one target capacity, it returns:

1. What you still need to ask the customer — graded 阻断(blocking) / 关键(critical) / 建议(recommended) /

可选(optional), each with a ready-to-send follow-up question and the reason it matters

1. What equipment to install — number of stages, per-stage size split, medium/fine hydraulic cone

crusher (model + cavity + CSS + unit count + power), primary crusher, screening area, belt width, auxiliaries

1. Annual output and mine service life
2. A 14-section proposal document — equipment list table, investment estimate, **assumptions & data

sources, risks & open items**, attachment list (flowsheet / layout / budget)

1. A product-size simulation of the resulting circuit — per-stage P80, circulating load, mass balance
2. A calibration loop that replaces literature default parameters with the user's own measured data

Why it exists

Three failure modes make AI untrustworthy at equipment selection: inventing parameters, skipping process steps, and presenting engineering rules of thumb as calibrated values. This project addresses each with a mechanism:

Mechanism · Implementation Data grading · Every value is tagged: vendor hard data / vendor historical / engineering range / gap Output carries its evidence · Every tool returns assumptions[] (assumption + source) and warnings[] Gaps are not fabricated · Unknown fields return null and appear in warnings[]; conflicting sources are kept side by side, never averaged Parameter provenance · Simulation outputs report whether parameters came from MACKORN-measured calibration, LITERATURE, or were user-supplied Numeric honesty · Every assumption is listed; parameter_source and calibration_basis are first-class output fields

Pricing note. Price bands in the tool output are MACKORN S4 historical reference ranges

(万元/台, 380 V domestic configuration) and are emitted together with an explicit disclaimer.

They are reference only and do not constitute a quotation — exchange rate, steel price,

voltage class, hydraulic-station configuration and delivery terms all materially affect the

final price. For a quotation, contact MACKORN sales (see Contact below).

报价说明。 工具输出中的价格区间是 MACKORN S4 历史参考区间（万元/台，380V 国产配置），

随输出附免责声明。仅供内部参考，不构成报价 —— 汇率、钢材价、电压等级、液压站配置、

交货条件均会显著影响最终价格。正式报价请联系 MACKORN 商务（见下方联系）。

Background and credibility

This is not a wrapper around an API. It encodes engineering practice from 29 years in the crushing and screening industry, and every number in it is traceable to a stated source.

Period · Experience 1993–1997 · China University of Mining and Technology — Mining Machinery Engineering, Metal Materials 1997–2004 · XCMG (徐工集团) — large-volume construction machinery manufacturing. Seven years of learning that volume production lives or dies on stability and service cost, and that cost-performance is the precondition, not the afterthought 2005–2007 · Sandvik Mining and Construction China — six months production training at Svedala, Sweden, then transferring that practice into the Shanghai plant; the full chain from material selection, smelting, manufacturing and quality control through assembly to after-sales, for hydraulic cone crushers 2007–present · Shanghai Mackorn Minerals (MACKORN 美矿) — 19 years of design, sales, field feedback and iteration on hydraulic cone crushers

What that means for the code, concretely:

• Vendor parameters come from MACKORN's own product data, not from a third party's materials.
• Engineering rules of thumb are labelled as ranges and never presented as calibrated values.
• mackorn_calibrate exists so a user can replace the literature default parameters with their own

measured sieve analyses — the plugin is built to be corrected by field data, not to sound finished.

• Gaps return null and appear in warnings[]. Nothing is filled in to look complete.

Why a vertical-domain plugin belongs on this list

A survey of 50 entries in the awesome-dsh-plugin list (2026-09) shows the catalogue is overwhelmingly developer tooling:

ui 8 · security 6 · usage 6 · wsl 4 · memory 4 · workflow 4
voice 3 · dev 3 · model 3 · browser 2 · tools 2 · market 1 · theme 1 · session 1 · notify 1 · remote 1

tools accounts for 2 of those 50, and there is no entry for mining, minerals processing, aggregates, or any other heavy-industry vertical.

That gap is what this plugin addresses. In this domain, the knowledge an AI actually needs — which cavity suits a given feed size, what CSS produces a target P80, how many units a closed circuit requires, what the mass balance and circulating load look like, which required data are missing and must be asked of the customer — exists today only inside vendor manuals and in individual engineers' heads. Putting it behind 29 callable tools makes it available to any AI a mining customer already uses, in the language they speak.

The pattern generalises. If dsh acquires one such plugin per industry — each carrying that industry's hard constraints, its own calibrated data, and an explicit honesty contract about what it does not know — the harness becomes useful well beyond software development.

Tool catalog — inputs and outputs

All 29 tools share one input convention: all parameters are optional except those marked (required), and every response is JSON containing at least assumptions[] and warnings[].

Core sales flow

Tool · Input (key fields) · Output (key fields) mackorn_requirement_intake · capacity_tph (required), form_text (raw pasted form), max_feed_mm, ore_type, compressive_strength_mpa, moisture_pct, soil_content_pct, product_mm, production_method, hours_per_day, days_per_year, scope · extracted_fields[] (value + source + confidence), missing[] (level + question + why), normalized, plant_design, cone_selection, annual_output, derived_recommendations[], assumptions[], warnings[] mackorn_proposal · capacity_tph (required), form_text or the same structured fields, cost_model, cost_units, electricity_price, liner_life_hours, liner_cost_per_set, language · 14-section Markdown proposal: project overview · design basis · process flow · equipment selection + bill of materials · technical parameters · electrical & control · environment · civil & layout · supply scope · investment estimate · schedule · assumptions & sources · risks & open items · attachments mackorn_cone_selection · target_tph (required), max_feed_mm, target_product_mm, stage (中碎/细碎/超细碎/auto), ore, units, closed_circuit · candidates[] ranked: model, series, cavity, css_mm, capacity_tph[], capacity_after_circulating_load_tph, headroom_ratio, power_kw, p80_estimate_mm[], match_score, basis (S1 detailed table or series-interval approximation), warnings[] mackorn_plant_design · target_tph (required), max_feed_mm, target_product_mm, ore, closed_circuit, washing · total_reduction_ratio, stage_count, per-stage feed/product/reduction ratio, medium & fine cone selection, screen_area_m2, belt_width_mm, auxiliaries, assumptions[]

Engineering computation

Tool · Input (key fields) · Output (key fields) mackorn_capacity_check · model (required, NH200…NH895 / NS200…NS600), cavity (EC/C/MC/M/MF/F/EF/EFX/EEF), css (required) · capacity range t/h, CSS compliance, max feed limit, dimensions, weight, basis, warnings[] mackorn_mcfm_analysis · cumulative_retained (required, 8 values), feed_top_mm, target_product_mm, ore_note · MCFM value, optimum-window verdict (4.0–4.5), deviation, lever-chain adjustment advice mackorn_cost_estimate · model (required), units, tph, hours_per_year, electricity_price, load_factor, liner_life_hours, liner_cost_per_set, annual_rate, term_years · installed power, annual kWh, energy cost per tonne, liner cost per tonne, financing monthly payment, exclusions list mackorn_wear_design · sections, wear_rates[], base_hardness, gradient_factor · wear uniformity index, multi-gradient zone hardness H_i, improvement over uniform design mackorn_grading_porosity · coarse_frac, mid_frac, fine_frac (all required) · bed porosity φ, optimal-blend comparison, stability verdict mackorn_equipment_catalog · *(none)* · all NH (9) / NS (4) models with max feed, CSS range, power, weight, capacity; 9 cavity codes and their applicability

Simulation (public-literature algorithms)

Tool · Input (key fields) · Output (key fields) mackorn_crusher_curve · css_mm (required), feed_p80_mm (required), feed_distribution (rosin-rammler / gaudin-schuhmann), feed_n, throw_mm, throw_factor, speed_rpm, phi, gamma, beta, bond_wi, ore, model, cavity · product_p80_mm, percentiles (P20/P50/P80), reduction_ratio, interlock zone K1_mm/K2_mm, sample_curve[], power (kWh/t), `parameter_source`, calibration_basis, mass_balance, assumptions[], warnings[] mackorn_simulate_flowsheet · feed_p80_mm (required), stages[] (required; each {type: crusher\ · screen, css_mm / aperture_mm, throw_mm, recirculate_to, screen_efficiency}), feed_n, bond_wi, ore, max_iter · converged, iterations, per-stage P80 / reduction ratio / fines fraction, circulating_load_ratio, final_p80_mm, mass_balance.yield_ratio (must equal 1.000000), power.total_kwh_per_t, `parameter_source`, calibrated_stages, assumptions[], warnings[] mackorn_calibrate · css_mm (required), feed_points[] (required, ≥2 × {size_mm, cum_pct}), product_points[] (required, ≥3 × {size_mm, cum_pct}), throw_mm, roughness · calibrated (φ/γ/β + interlock factor), fit (RMSE in percentage points, max deviation, evaluations, quality verdict), residuals[] per point, library_defaults for comparison, how_to_persist, assumptions[], warnings[] (incl. boundary-detection warning)

Knowledge, market and self-iteration

Tool · Input (key fields) · Output (key fields) mackorn_market_intel · focus, scores (five dimensions × score/weight/evidence) · five-dimension framework & scoring rubric, competitive-benchmark matrix, advantage/gap analysis, customer-pain talking points, quotation factors mackorn_selection_report · target_tph (required), max_feed_mm, target_product_mm, stage, ore, closed_circuit, cumulative_retained, include_cost, cost_model, include_wear · consolidated Markdown report: plant config + cone selection + MCFM + cost + wear, with aggregated assumptions & risks mackorn_intel_watch · focus (watch-item id or category) · 6 fixed watch items (Sandvik / Metso / China patents / international patents / standards / market), each with *why watch · which sources · search terms · cadence · ingestion format · credibility rubric* mackorn_knowledge_update · entries[] (required; each needs title, content, **source_url**), tolerance, check_fields, actor, rationale, crushing_leverage_score · ingestion result, credibility score, numeric-conflict ledger, version-evolution verdict, changelog. Entries without a source URL are rejected. mackorn_pdca_status · action (status/record), plan, do_items, check, act, actor · model version, knowledge revision, entry count & credibility distribution, conflict ledger, four evolution metrics, due watch items, optimization suggestions mackorn_contact · language (12 languages), include_partner, include_triggers · company name, address (CN/EN), service times, sales contacts, WeChat QR asset, worldwide distributor/agent recruitment programme; optional trigger-coverage report

Machine-readable usage contract

Calling convention

// request  — every field except "(required)" is optional
{ "name": "mackorn_cone_selection",
  "arguments": { "target_tph": 500, "max_feed_mm": 180, "target_product_mm": 20, "stage": "中碎" } }

Response convention (all tools)

{
  "…": "tool-specific result fields",
  "assumptions": [ { "assumption": "…", "source": "S1 | ENGINEERING-RANGE | ENGINEERING-DEFAULT | LITERATURE | MACKORN-实测" } ],
  "warnings":    [ "…" ],          // empty array when none — never omitted
  "parameter_source": "MACKORN-实测标定 | LITERATURE | USER",   // simulation tools
  "basis": "S1 腔型×CSS 详表 | 系列区间近似"                    // selection tools
}

Rules an AI client should respect when relaying results:

1. Always relay assumptions[] and warnings[] to the user — they are part of the answer, not metadata
2. Treat basis: 系列区间近似 as requiring technical review, not as a final figure
3. Treat reference price ranges as reference only; they are not quotations
4. Never present a simulated P80 as a guaranteed contract value — it must be backed by calibrated,

field-verified data

1. If a field is null, say it is unknown; do not fill it in

Three ways to use it

1. DeepSeek Harness plugin

# Way A — local install script (recommended, effective without restart)
powershell -ExecutionPolicy Bypass -File .\tools\install.ps1
powershell -ExecutionPolicy Bypass -File .\tools\verify.ps1 -BootTest

# Way B — $DSH_HOME/cordis.patch.yml
- insert:
    - id: mackorn-cone-crusher
      name: './plugins/mackorn-cone-crusher/index.mjs'

⚠️ Measured result: an absolute path inside the patch is silently ignored — use a package name

or a ./ path relative to the patch file's own directory.

2. MCP server — any MCP-capable AI client

node plugin\mcp-server.mjs --list        # list all 29 tools
node plugin\mcp-server.mjs --selftest    # protocol + every tool, self-check
node plugin\mcp-server.mjs               # start the stdio server

{
  "mcpServers": {
    "mackorn": { "command": "node", "args": ["/absolute/path/to/plugin/mcp-server.mjs"] }
  }
}

Zero dependencies, hand-written JSON-RPC over stdio — no MCP SDK required. Implements initialize / tools/list / tools/call / ping / resources/list / prompts/list.